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Last updated:

December 2, 2022

Duration:

Unlimited Duration

FREE

This course includes:

Unlimited Duration

Badge on Completion

Certificate of completion

Unlimited Duration

Description

This course aims to give students the tools and training to recognize convex optimization problems that arise in scientific and engineering applications, presenting the basic theory, and concentrating on modeling aspects and results that are useful in applications.

Topics include convex sets, convex functions, optimization problems, least-squares, linear and quadratic programs, semidefinite programming, optimality conditions, and duality theory. Applications to signal processing, control, machine learning, finance, digital and analog circuit design, computational geometry, statistics, and mechanical engineering are presented. Students complete hands-on exercises using high-level numerical software.

Acknowledgements

The course materials were developed jointly by Prof. Stephen Boyd (Stanford), who was a visiting professor at MIT when this course was taught, and Prof. Lieven Vanderberghe (UCLA).

Course Curriculum

  • Introduction Unlimited
  • Convex sets Unlimited
  • Convex functions Unlimited
  • Convex optimization problems Unlimited
  • Duality Unlimited
  • Approximation and fitting Unlimited
  • Statistical estimation Unlimited
  • Geometric problems Unlimited
  • Filter design and equalization Unlimited
  • Miscellaneous applications Unlimited
  • l1 methods for convex-cardinality problems Unlimited
  • l1 methods for convex-cardinality problems (cont.) Unlimited
  • Stochastic programming Unlimited
  • Chance constrained optimization Unlimited
  • Numerical linear algebra background Unlimited
  • Unconstrained minimization Unlimited
  • Equality constrained minimization Unlimited
  • Interior-point methods Unlimited
  • Disciplined convex programming and CVX Unlimited

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Massachusetts Institute of Technology